Handwriting recognition systems on devices such as Tablet PCs or Pocket PCs typically employ machine learning models to produce good walkup accuracy for a large variety of writing styles but errors still occur for some individual writing styles. Personalizing a handwriting recognizer using explicit samples where the user confirms the true translation of the sample is time consuming and thus many users either forego this step or provide insufficient samples. A better approach is to use implicit samples collected while the user goes about their normal tasks. To effectively make use of implicit data, good filtering techniques are needed to distinguish the cases where the recognized result is more likely to be correct from the instances when they are incorrect